I found the title interesting initially, because I've noticed a similar trend in the development of my cellular automata algorithms. I was surprised to see a mention early on in the article.
I think I've personally experienced the emergent concept they're describing through my tinkering with cellular automata.
As a preface, my totalisitic cellular automata use the same family/type of transition function as Conway's GoL (with larger neighbourhoods and more complex rule parameters)
I noticed the complexity of the emergent structures or 'agents' of the algorithms increases dramatically when the rule allows for the survival of 'dust' in the environment instead of blank/empty space.
The 'dust' usually consists of individual/unbound/unused pixels (or a light crystalline grid) that do not 'act' under their own power/energy, but simply survive to the next generation unchanged. This creates a probabilistic gradient-like environment for more self-contained/well-defined 'agents' to interact with. The interactions usually manifest as the consumption of nearby dust, causing/allowing movement for the agent.
Multiple 'species' of agents in the same uniformly applied algorithm count as environment too, right? Well, those interactions boost complexity as well. The little replicating balls leave behind a dust trail, often leading a large orb to its present location, where they then.. er.. 'interact'.
Videos (Best viewed in HD)
2 main emergent types of 'agents' interacting with the 'dust' and each other:
Using a 'light crystalline form' instead of 'dust':
I would say that a complex environment leads to additional potentially complex emergent behaviour in the CA simulations. The emergent factors relating to more complex environments described both here and in the article might be related.
Thoughts?